/*
 * Encog(tm) Examples v2.4
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 * 
 * Copyright 2008-2010 by Heaton Research Inc.
 * 
 * Released under the LGPL.
 *
 * This is free software; you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2.1 of
 * the License, or (at your option) any later version.
 *
 * This software is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this software; if not, write to the Free
 * Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
 * 02110-1301 USA, or see the FSF site: http://www.fsf.org.
 * 
 * Encog and Heaton Research are Trademarks of Heaton Research, Inc.
 * For information on Heaton Research trademarks, visit:
 * 
 * http://www.heatonresearch.com/copyright.html
 */

package mtamarket;

import java.io.File;

import org.encog.Encog;
import org.encog.ml.data.MLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.persist.EncogDirectoryPersistence;

/**
 * Load the training data from an Encog file, produced during the
 * "build training step", and attempt to train.
 * 
 * @author jeff
 * 
 */
public class MarketTrain {

	public static void train(Config config, File dataDir, String networkName) {


		
		BasicNetwork network = BobsUtility.getNetwork(networkName);

		final MLDataSet trainingSet = MarketPrune.getMLDataSet(config.transformedData);

		// train the neural network
		EncogUtility.trainConsole(network, trainingSet, Config.TRAINING_MINUTES);
						
		System.out.println("Final Error: " + network.calculateError(trainingSet));
		System.out.println("Training complete, saving network.");
		BobsUtility.saveNetwork(network);
		System.out.println("Network saved.");
		
		Encog.getInstance().shutdown();

	}
}
